A.4 Neurological events following COVID-19 vaccination: does ethnicity matter?
Bibliographic record
Abstract
Background: Neurological complications following vaccinations have been described before, but the rates of neurological complications, and their variation by ethnicity, following COVID-19 vaccine are not well-known. Methods: We conducted a population-based cohort study of Ontarians aged 18 years and over who received their first COVID-19 vaccine, and followed them for six weeks to estimate the incidence of neurological events, ascertained using validated case definitions based on ICD-10 codes. Ethnicity was defined using last name surname algorithm. We used multivariable logistic regression models, adjusting for age, sex, and vaccine-type to evaluate ethnic differences. Results: In the included 10,063,466 Ontario residents, incidence of GBS (n=72), CVST (n=52) and transverse myelitis (n=25) after first COVID-19 vaccine was rare. The crude rate of ischemic stroke (240/1,000,000 people) was the highest followed by Bell’s palsy (54/1,000,000). Compared to the general population, the adjusted odds of ischemic stroke and Bell’s palsy were lower in Chinese (aOR Bell’s 0.62; 0.39-0.98 and a OR ischemic stroke 0.74; 0.59-0.91) and South Asians (aOR Bell’s 0.83; 0.52-1.31 and aOR ischemic stroke 0.84; 0.65-1.08). Conclusions: The incidence of neurological events following COVID-19 vaccine is low, and it varies by ethnicity. Our findings should encourage vaccination against COVID-19 in all ethnic groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".